Group 1 - The core viewpoint of the article emphasizes that in the second half of AI development, model evaluation (Evals) is more critical than model training, necessitating a fundamental rethink of evaluation methods [2][3] - The industry is transitioning from conceptual validation of AI to building systems that can define, measure, and solve problems through experience and clarity, making Evals a crucial aspect of AI product development [2][3] - The article introduces a practical guide on Evals, detailing three evaluation methods, how to construct and iterate an Evals system, and considerations for Evals design [2][3] Group 2 - Evals are essential for measuring the quality and effectiveness of AI systems, serving as a clear standard for what constitutes a "good" AI product, beyond traditional software testing metrics [9][10] - Evaluating AI systems resembles a driving test more than traditional software testing, focusing on perception, decision-making, and safety rather than deterministic outcomes [10][11] - The article outlines three methods for Evals: manual Evals, code-based Evals, and LLM-based Evals, each with its advantages and disadvantages [13][15][17] Group 3 - Common evaluation areas include toxicity/tone, overall correctness, hallucination, code generation, summary quality, and retrieval relevance [21][22][23] - A successful LLM Eval consists of four components: setting the role, providing context, clarifying goals, and defining terms and labels [24] - The process of building an Eval is iterative, involving data collection, initial assessment, iterative cycles, and production environment monitoring [25][35] Group 4 - Common mistakes in Evals design include overcomplicating initial designs, neglecting edge cases, and failing to validate results with real user feedback [37] - Companies are encouraged to start with a key feature for evaluation, such as hallucination detection, and to iteratively refine their Evals prompts based on real interaction data [42]